It is no news that today, digitalization, data-driven services, and the associated new business models, are pivotal to a company’s success.
The changes we see, especially in the manufacturing sector, are faster than ever before. Shorter times to market, greater flexibility, and increased efficiency while ensuring product quality, are just some of the challenges companies face.
And while smart factories are already a reality in many places, companies need to further develop and use data-based business models, digitalize the whole product lifecycle from cradle to grave, and integrate business and manufacturing systems from the “top floor” to the “shop floor” to fully benefit from the potential of Industry 4.0.
Smart technologies drive the production of tomorrow
In a smart factory, production processes are connected, meaning that machines, interfaces, and components communicate with one another. The large amounts of data that are created can then be used to optimize the manufacturing process.
Machine Learning, for example, enables predictions to be made based on large amounts of data. It is built upon pattern recognition and can independently draw knowledge from experience.
With the help of properly analyzed customer, log, and sensor data, new solutions can be found, processes can be made more efficient, and sometimes it even enables the creation of new business models.
Artificial Intelligence (AI) – the broader concept of machines that can carry out tasks in a smart way – is expected to have an enormous impact on many of the greatest societal challenges.
McKinsey estimates that AI techniques have the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. In terms of Industry 4.0, AI can help companies to merge industrial data and transform it into innovative, data-based products and services.
Keeping up with the giants
Europe could have a leading position in AI. European researchers have excellent scientific standing, global companies are investing in Europe, and Europe is developing a vibrant startup community. However, AI resources are scattered throughout Europe, and international competition is fierce. As an example, some 48% of all AI venture funding globally went to China in 2017.
To fully exploit the potential of AI for the benefit of the European economy and society, and to guarantee Europe’s leading position in AI, it is essential to join forces at the regional level to capitalize on its strengths.
If we look at the platforms that enable AI, Europe faces the mammoth task of catching up with today’s giants.
Think about DeepMind’s AlphaGo Zero. This programme was truly a milestone: it beat the prior version of AlphaGo – which had itself beaten 17-time world champion Lee Sedol 100–0, and it was trained without any data from real human games.
Previous versions of AlphaGo were trained how to play Go by thousands of human amateur and professional games. AlphaGo Zero skipped this step and learned to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play.
With AlphaGo Zero, we saw something like “artificial intuition” for the first time. Why shouldn’t we be able to develop this kind of intuition – based on machine data – to control production machines?
With this comes a new mode of competition because no company can achieve this alone. For many technology companies, so-called “coopetition” – the act of cooperation between competing companies – is nothing new.
Without partners like Amazon Web Services, Microsoft Azure, Google Cloud Platform, or SAP Cloud Platform, industry platforms like Mindsphere, Axoon, or Bosch’s IoT (Internet of Things) Suite wouldn’t be possible.
I am convinced that the manufacturing industry will need to develop this type of strategic alliance soon. To reach a critical mass and competence across the board, industry providers need to actively cooperate with their competitors and create platforms together.
There is a direct correlation between digital readiness and AI readiness, and because it takes technology to make technology, digital infrastructure is another critical requirement.
AI systems require vast amounts of computing power, meaning servers or cloud computing services with access to multi-core processors and graphics processing units. In addition, to train machine learning systems, a lot of data is needed, and that means more storage capacity as well.
Technology for the people
Seth W. Godin, American author and former dotcom business executive, said: “No organization ever created an innovation. People innovate, not companies.”
So, we must not forget the global war for talent. Nearly every company is thinking about how AI can positively impact their businesses, so they are all on the hunt for professionals to help them make their vision a reality. In the future, companies of every size and from every industry will compete for the best AI talent.
We also need to take concerns over job losses seriously, but focus on the potential upside – because, while some 75 million jobs could be displaced by 2022, machines and algorithms in the workplace are expected to create 133 million new roles, according to the World Economic Forum’s report, “The Future of Jobs 2018”. This means that the growth of artificial intelligence could create 58 million net new jobs in the next few years.
Here is my call to action: we all must help to ensure that smart machines work to serve humans, rather than compete with them. It is up to all of us to use technological advancements to tackle the world’s greatest challenges and turn them into our biggest opportunities.
Let’s jointly create a world where AI augments humanity, where technology frees us from dangerous or repetitive tasks, and lifts all people up to unleash their greatest potential.